Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations76518
Missing cells0
Missing cells (%)0.0%
Duplicate rows406
Duplicate rows (%)0.5%
Total size in memory9.3 MiB
Average record size in memory128.0 B

Variable types

Numeric11
Categorical5

Alerts

Dataset has 406 (0.5%) duplicate rowsDuplicates
Admission grade is highly overall correlated with Previous qualification (grade)High correlation
Age at enrollment is highly overall correlated with Application mode and 1 other fieldsHigh correlation
Application mode is highly overall correlated with Age at enrollmentHigh correlation
Course is highly overall correlated with Daytime/evening attendanceHigh correlation
Daytime/evening attendance is highly overall correlated with Age at enrollment and 1 other fieldsHigh correlation
Father's occupation is highly overall correlated with Mother's occupationHigh correlation
Mother's occupation is highly overall correlated with Father's occupationHigh correlation
Previous qualification (grade) is highly overall correlated with Admission gradeHigh correlation
Daytime/evening attendance is highly imbalanced (58.2%)Imbalance
Tuition fees up to date is highly imbalanced (51.1%)Imbalance
Mother's occupation has 2206 (2.9%) zerosZeros
Father's occupation has 2056 (2.7%) zerosZeros

Reproduction

Analysis started2025-11-17 08:43:58.416847
Analysis finished2025-11-17 08:44:03.538480
Duration5.12 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Marital status
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1119344
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:03.559845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44166893
Coefficient of variation (CV)0.3972077
Kurtosis34.866324
Mean1.1119344
Median Absolute Deviation (MAD)0
Skewness5.3828525
Sum85083
Variance0.19507144
MonotonicityNot monotonic
2025-11-17T11:44:03.582294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
170189
91.7%
25296
 
6.9%
4866
 
1.1%
5116
 
0.2%
635
 
< 0.1%
316
 
< 0.1%
ValueCountFrequency (%)
170189
91.7%
25296
 
6.9%
316
 
< 0.1%
4866
 
1.1%
5116
 
0.2%
635
 
< 0.1%
ValueCountFrequency (%)
635
 
< 0.1%
5116
 
0.2%
4866
 
1.1%
316
 
< 0.1%
25296
 
6.9%
170189
91.7%

Application mode
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.054419
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:03.606685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median17
Q339
95-th percentile43
Maximum53
Range52
Interquartile range (IQR)38

Descriptive statistics

Standard deviation16.682337
Coefficient of variation (CV)1.0391119
Kurtosis-1.2418586
Mean16.054419
Median Absolute Deviation (MAD)16
Skewness0.5962323
Sum1228452
Variance278.30037
MonotonicityNot monotonic
2025-11-17T11:44:03.636795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
135227
46.0%
1716523
21.6%
3914519
19.0%
443012
 
3.9%
432671
 
3.5%
71498
 
2.0%
181256
 
1.6%
42551
 
0.7%
51442
 
0.6%
16265
 
0.3%
Other values (12)554
 
0.7%
ValueCountFrequency (%)
135227
46.0%
28
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
587
 
0.1%
71498
 
2.0%
91
 
< 0.1%
1043
 
0.1%
121
 
< 0.1%
15183
 
0.2%
ValueCountFrequency (%)
53225
 
0.3%
51442
 
0.6%
443012
 
3.9%
432671
 
3.5%
42551
 
0.7%
3914519
19.0%
351
 
< 0.1%
272
 
< 0.1%
261
 
< 0.1%
181256
 
1.6%

Course
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9001.2864
Minimum33
Maximum9991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:03.662651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile8014
Q19119
median9254
Q39670
95-th percentile9991
Maximum9991
Range9958
Interquartile range (IQR)551

Descriptive statistics

Standard deviation1803.4385
Coefficient of variation (CV)0.20035342
Kurtosis19.081583
Mean9001.2864
Median Absolute Deviation (MAD)246
Skewness-4.4810164
Sum6.8876043 × 108
Variance3252390.5
MonotonicityNot monotonic
2025-11-17T11:44:03.687517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
950012074
15.8%
97738214
10.7%
92387935
10.4%
91477741
10.1%
92545425
 
7.1%
90855373
 
7.0%
96704760
 
6.2%
99914057
 
5.3%
90033733
 
4.9%
90703281
 
4.3%
Other values (9)13925
18.2%
ValueCountFrequency (%)
3372
 
0.1%
391
 
< 0.1%
1712859
3.7%
9791
 
< 0.1%
80142438
3.2%
90033733
4.9%
90703281
4.3%
90855373
7.0%
91193004
3.9%
91301606
 
2.1%
ValueCountFrequency (%)
99914057
 
5.3%
98533198
 
4.2%
97738214
10.7%
96704760
 
6.2%
9556746
 
1.0%
950012074
15.8%
92545425
7.1%
92387935
10.4%
91477741
10.1%
91301606
 
2.1%

Daytime/evening attendance
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.9 KiB
1
70038 
0
 
6480

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters76518
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
170038
91.5%
06480
 
8.5%

Length

2025-11-17T11:44:03.712744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-17T11:44:03.734217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
170038
91.5%
06480
 
8.5%

Most occurring characters

ValueCountFrequency (%)
170038
91.5%
06480
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
170038
91.5%
06480
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
170038
91.5%
06480
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
170038
91.5%
06480
 
8.5%

Previous qualification
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.65876
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:03.753403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile19
Maximum43
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.6237736
Coefficient of variation (CV)2.3570208
Kurtosis10.793372
Mean3.65876
Median Absolute Deviation (MAD)0
Skewness3.4362448
Sum279961
Variance74.369472
MonotonicityNot monotonic
2025-11-17T11:44:03.781657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
167183
87.8%
192947
 
3.9%
392928
 
3.8%
31401
 
1.8%
12899
 
1.2%
9308
 
0.4%
40259
 
0.3%
42229
 
0.3%
298
 
0.1%
698
 
0.1%
Other values (11)168
 
0.2%
ValueCountFrequency (%)
167183
87.8%
298
 
0.1%
31401
 
1.8%
423
 
< 0.1%
53
 
< 0.1%
698
 
0.1%
9308
 
0.4%
1043
 
0.1%
112
 
< 0.1%
12899
 
1.2%
ValueCountFrequency (%)
4339
 
0.1%
42229
 
0.3%
40259
 
0.3%
392928
3.8%
3838
 
< 0.1%
373
 
< 0.1%
361
 
< 0.1%
192947
3.9%
172
 
< 0.1%
1511
 
< 0.1%

Previous qualification (grade)
Real number (ℝ)

High correlation 

Distinct110
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.37877
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:03.812281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile115
Q1125
median133.1
Q3140
95-th percentile150
Maximum190
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.995328
Coefficient of variation (CV)0.083059605
Kurtosis1.1647593
Mean132.37877
Median Absolute Deviation (MAD)6.9
Skewness0.22156013
Sum10129358
Variance120.89724
MonotonicityNot monotonic
2025-11-17T11:44:03.847079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133.110719
 
14.0%
1306261
 
8.2%
1405917
 
7.7%
1204511
 
5.9%
1252749
 
3.6%
1352527
 
3.3%
1312197
 
2.9%
1502151
 
2.8%
1322097
 
2.7%
1271753
 
2.3%
Other values (100)35636
46.6%
ValueCountFrequency (%)
953
 
< 0.1%
9610
 
< 0.1%
974
 
< 0.1%
9915
 
< 0.1%
100684
0.9%
10127
 
< 0.1%
10244
 
0.1%
10317
 
< 0.1%
10529
 
< 0.1%
10694
 
0.1%
ValueCountFrequency (%)
1901
 
< 0.1%
1885
 
< 0.1%
184.42
 
< 0.1%
1841
 
< 0.1%
1824
 
< 0.1%
18039
0.1%
1783
 
< 0.1%
17721
< 0.1%
1762
 
< 0.1%
1755
 
< 0.1%

Mother's qualification
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.837633
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:03.878060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median19
Q337
95-th percentile38
Maximum44
Range43
Interquartile range (IQR)36

Descriptive statistics

Standard deviation15.399456
Coefficient of variation (CV)0.77627489
Kurtosis-1.6466933
Mean19.837633
Median Absolute Deviation (MAD)18
Skewness-0.045535037
Sum1517936
Variance237.14326
MonotonicityNot monotonic
2025-11-17T11:44:03.906172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
120202
26.4%
1918980
24.8%
3718664
24.4%
389059
11.8%
35890
 
7.7%
341993
 
2.6%
2613
 
0.8%
12358
 
0.5%
4313
 
0.4%
599
 
0.1%
Other values (25)347
 
0.5%
ValueCountFrequency (%)
120202
26.4%
2613
 
0.8%
35890
 
7.7%
4313
 
0.4%
599
 
0.1%
621
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
945
 
0.1%
1015
 
< 0.1%
ValueCountFrequency (%)
442
 
< 0.1%
4316
 
< 0.1%
4219
 
< 0.1%
4126
 
< 0.1%
4049
 
0.1%
3946
 
0.1%
389059
11.8%
3718664
24.4%
3615
 
< 0.1%
3510
 
< 0.1%

Father's qualification
Real number (ℝ)

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.425076
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:03.937444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median19
Q337
95-th percentile38
Maximum44
Range43
Interquartile range (IQR)33

Descriptive statistics

Standard deviation14.921164
Coefficient of variation (CV)0.63697398
Kurtosis-1.4139802
Mean23.425076
Median Absolute Deviation (MAD)18
Skewness-0.44033297
Sum1792440
Variance222.64114
MonotonicityNot monotonic
2025-11-17T11:44:03.966699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3723290
30.4%
1919015
24.9%
115604
20.4%
3812253
16.0%
33011
 
3.9%
341790
 
2.3%
2496
 
0.6%
12294
 
0.4%
4224
 
0.3%
39120
 
0.2%
Other values (29)421
 
0.6%
ValueCountFrequency (%)
115604
20.4%
2496
 
0.6%
33011
 
3.9%
4224
 
0.3%
5100
 
0.1%
69
 
< 0.1%
72
 
< 0.1%
919
 
< 0.1%
1010
 
< 0.1%
1175
 
0.1%
ValueCountFrequency (%)
443
 
< 0.1%
4314
 
< 0.1%
427
 
< 0.1%
4113
 
< 0.1%
4025
 
< 0.1%
39120
 
0.2%
3812253
16.0%
3723290
30.4%
3643
 
0.1%
355
 
< 0.1%

Mother's occupation
Real number (ℝ)

High correlation  Zeros 

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5831961
Minimum0
Maximum194
Zeros2206
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:03.996021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median7
Q39
95-th percentile9
Maximum194
Range194
Interquartile range (IQR)5

Descriptive statistics

Standard deviation17.471591
Coefficient of variation (CV)2.0355577
Kurtosis65.685406
Mean8.5831961
Median Absolute Deviation (MAD)2
Skewness7.6969749
Sum656769
Variance305.25648
MonotonicityNot monotonic
2025-11-17T11:44:04.028397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
932386
42.3%
416062
21.0%
59452
 
12.4%
34644
 
6.1%
74207
 
5.5%
24087
 
5.3%
02206
 
2.9%
90978
 
1.3%
6786
 
1.0%
1766
 
1.0%
Other values (30)944
 
1.2%
ValueCountFrequency (%)
02206
 
2.9%
1766
 
1.0%
24087
 
5.3%
34644
 
6.1%
416062
21.0%
59452
 
12.4%
6786
 
1.0%
74207
 
5.5%
8243
 
0.3%
932386
42.3%
ValueCountFrequency (%)
19479
 
0.1%
19313
 
< 0.1%
19223
 
< 0.1%
191212
0.3%
17511
 
< 0.1%
1732
 
< 0.1%
1721
 
< 0.1%
1711
 
< 0.1%
1631
 
< 0.1%
1538
 
< 0.1%

Father's occupation
Real number (ℝ)

High correlation  Zeros 

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8821715
Minimum0
Maximum195
Zeros2056
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:04.064279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q39
95-th percentile10
Maximum195
Range195
Interquartile range (IQR)4

Descriptive statistics

Standard deviation16.80394
Coefficient of variation (CV)1.891873
Kurtosis61.88138
Mean8.8821715
Median Absolute Deviation (MAD)2
Skewness7.4906137
Sum679646
Variance282.37241
MonotonicityNot monotonic
2025-11-17T11:44:04.100243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
922320
29.2%
712910
16.9%
59661
12.6%
46668
 
8.7%
35663
 
7.4%
85111
 
6.7%
104107
 
5.4%
62922
 
3.8%
22236
 
2.9%
02056
 
2.7%
Other values (46)2864
 
3.7%
ValueCountFrequency (%)
02056
 
2.7%
11177
 
1.5%
22236
 
2.9%
35663
 
7.4%
46668
 
8.7%
59661
12.6%
62922
 
3.8%
712910
16.9%
85111
 
6.7%
922320
29.2%
ValueCountFrequency (%)
1955
 
< 0.1%
1948
 
< 0.1%
19383
0.1%
19227
 
< 0.1%
1911
 
< 0.1%
1838
 
< 0.1%
18211
 
< 0.1%
18119
 
< 0.1%
17537
< 0.1%
1743
 
< 0.1%

Admission grade
Real number (ℝ)

High correlation 

Distinct668
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.36397
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:04.135245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile104.3
Q1118
median124.6
Q3132
95-th percentile149.4
Maximum190
Range95
Interquartile range (IQR)14

Descriptive statistics

Standard deviation12.562328
Coefficient of variation (CV)0.10020685
Kurtosis0.8146485
Mean125.36397
Median Absolute Deviation (MAD)7
Skewness0.40255668
Sum9592600.4
Variance157.81209
MonotonicityNot monotonic
2025-11-17T11:44:04.186629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1202995
 
3.9%
1302704
 
3.5%
1402623
 
3.4%
1001683
 
2.2%
1101482
 
1.9%
150986
 
1.3%
128.2692
 
0.9%
126.3604
 
0.8%
116.5598
 
0.8%
121591
 
0.8%
Other values (658)61560
80.5%
ValueCountFrequency (%)
95294
0.4%
95.129
 
< 0.1%
95.542
 
0.1%
95.72
 
< 0.1%
95.819
 
< 0.1%
96118
0.2%
96.112
 
< 0.1%
96.52
 
< 0.1%
96.731
 
< 0.1%
97146
0.2%
ValueCountFrequency (%)
19011
< 0.1%
184.44
 
< 0.1%
1843
 
< 0.1%
180.42
 
< 0.1%
18015
< 0.1%
179.611
< 0.1%
178.61
 
< 0.1%
178.35
 
< 0.1%
1788
< 0.1%
176.711
< 0.1%

Tuition fees up to date
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.9 KiB
1
68380 
0
8138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters76518
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
168380
89.4%
08138
 
10.6%

Length

2025-11-17T11:44:04.229607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-17T11:44:04.249518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
168380
89.4%
08138
 
10.6%

Most occurring characters

ValueCountFrequency (%)
168380
89.4%
08138
 
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
168380
89.4%
08138
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
168380
89.4%
08138
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
168380
89.4%
08138
 
10.6%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.9 KiB
0
52352 
1
24166 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters76518
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
052352
68.4%
124166
31.6%

Length

2025-11-17T11:44:04.269613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-17T11:44:04.285310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
052352
68.4%
124166
31.6%

Most occurring characters

ValueCountFrequency (%)
052352
68.4%
124166
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
052352
68.4%
124166
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
052352
68.4%
124166
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
052352
68.4%
124166
31.6%

Scholarship holder
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.9 KiB
0
57588 
1
18930 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters76518
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
057588
75.3%
118930
 
24.7%

Length

2025-11-17T11:44:04.305540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-17T11:44:04.321120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
057588
75.3%
118930
 
24.7%

Most occurring characters

ValueCountFrequency (%)
057588
75.3%
118930
 
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
057588
75.3%
118930
 
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
057588
75.3%
118930
 
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)76518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
057588
75.3%
118930
 
24.7%

Age at enrollment
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.278653
Minimum17
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size597.9 KiB
2025-11-17T11:44:04.344950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q118
median19
Q323
95-th percentile38
Maximum70
Range53
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.8892411
Coefficient of variation (CV)0.30923059
Kurtosis5.8161706
Mean22.278653
Median Absolute Deviation (MAD)1
Skewness2.3697288
Sum1704718
Variance47.461643
MonotonicityNot monotonic
2025-11-17T11:44:04.377635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1822377
29.2%
1918078
23.6%
2010367
13.5%
214516
 
5.9%
221907
 
2.5%
241834
 
2.4%
251662
 
2.2%
271544
 
2.0%
281358
 
1.8%
261300
 
1.7%
Other values (36)11575
15.1%
ValueCountFrequency (%)
1740
 
0.1%
1822377
29.2%
1918078
23.6%
2010367
13.5%
214516
 
5.9%
221907
 
2.5%
231042
 
1.4%
241834
 
2.4%
251662
 
2.2%
261300
 
1.7%
ValueCountFrequency (%)
7020
 
< 0.1%
623
 
< 0.1%
6110
 
< 0.1%
6017
 
< 0.1%
598
 
< 0.1%
5864
0.1%
5713
 
< 0.1%
5560
0.1%
5434
< 0.1%
5347
0.1%

Target
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.9 KiB
Graduate
36282 
Dropout
25296 
Enrolled
14940 

Length

Max length8
Median length8
Mean length7.6694111
Min length7

Characters and Unicode

Total characters586848
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate
2nd rowDropout
3rd rowDropout
4th rowEnrolled
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate36282
47.4%
Dropout25296
33.1%
Enrolled14940
19.5%

Length

2025-11-17T11:44:04.409751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-17T11:44:04.427879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
graduate36282
47.4%
dropout25296
33.1%
enrolled14940
19.5%

Most occurring characters

ValueCountFrequency (%)
r76518
13.0%
a72564
12.4%
o65532
11.2%
u61578
10.5%
t61578
10.5%
d51222
8.7%
e51222
8.7%
G36282
6.2%
l29880
 
5.1%
D25296
 
4.3%
Other values (3)55176
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)586848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r76518
13.0%
a72564
12.4%
o65532
11.2%
u61578
10.5%
t61578
10.5%
d51222
8.7%
e51222
8.7%
G36282
6.2%
l29880
 
5.1%
D25296
 
4.3%
Other values (3)55176
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)586848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r76518
13.0%
a72564
12.4%
o65532
11.2%
u61578
10.5%
t61578
10.5%
d51222
8.7%
e51222
8.7%
G36282
6.2%
l29880
 
5.1%
D25296
 
4.3%
Other values (3)55176
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)586848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r76518
13.0%
a72564
12.4%
o65532
11.2%
u61578
10.5%
t61578
10.5%
d51222
8.7%
e51222
8.7%
G36282
6.2%
l29880
 
5.1%
D25296
 
4.3%
Other values (3)55176
9.4%

Interactions

2025-11-17T11:44:03.029306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.401193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.748285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.092177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.466656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.820166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.156620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.502712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.899256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.262687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.617665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.058178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.437452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.778464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.121794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.499399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.850493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.184881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.570131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.930758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.294099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.645834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.087242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.470250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.806897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.151563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.528409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.880222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.213371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.602253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.964162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.325584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.674288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.116498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.502406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.836784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.186215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.558955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.912828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.242163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.635934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.000843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.357448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.702845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.144954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.532577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.868660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.236769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.586805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.941961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.269747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.670622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.032480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.391639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.731323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.174733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.563582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.899105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.273477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.615376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.970164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.298883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.706102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.066652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.423536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.763168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.206347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.593137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.929577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.309365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.643313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.000551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.330769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.744916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.098654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.454579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.881325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.235321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.622303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.962667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.339996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.671570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.031683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.366623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.773896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.129992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.485728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.909484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.266959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.653791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.996067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.373864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.706296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.063029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.400148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.810150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.163408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.519015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.940574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.298810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.687638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.029549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.406285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.736779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.094714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.432046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.841252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.199835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.551879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.971131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:03.327446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:43:59.716286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.062630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.435356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:00.789266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.126841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.467491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:01.870122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.231272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.586125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-17T11:44:02.998138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-17T11:44:04.452059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Admission gradeAge at enrollmentApplication modeCourseDaytime/evening attendanceFather's occupationFather's qualificationGenderMarital statusMother's occupationMother's qualificationPrevious qualificationPrevious qualification (grade)Scholarship holderTargetTuition fees up to date
Admission grade1.000-0.179-0.081-0.0300.118-0.021-0.0080.101-0.041-0.019-0.0290.0830.5820.1210.1700.122
Age at enrollment-0.1791.0000.574-0.1150.5160.0700.0780.2440.4410.1340.1670.388-0.1510.2290.2820.286
Application mode-0.0810.5741.000-0.1120.457-0.0090.0450.2630.3160.0440.1070.440-0.0530.2400.2880.282
Course-0.030-0.115-0.1121.0000.594-0.0100.0110.1090.023-0.0140.016-0.173-0.0400.0420.1200.032
Daytime/evening attendance0.1180.5160.4570.5941.0000.0560.1980.0770.4060.0710.2360.1630.1480.1050.1310.099
Father's occupation-0.0210.070-0.009-0.0100.0561.0000.2690.0490.0680.5050.205-0.014-0.0210.0590.0910.073
Father's qualification-0.0080.0780.0450.0110.1980.2691.0000.0910.1210.3120.4660.0110.0040.1450.1350.083
Gender0.1010.2440.2630.1090.0770.0490.0911.0000.0710.0570.0870.1460.0720.2280.3300.172
Marital status-0.0410.4410.3160.0230.4060.0680.1210.0711.0000.1220.1940.201-0.0260.1050.1060.127
Mother's occupation-0.0190.1340.044-0.0140.0710.5050.3120.0570.1221.0000.4250.013-0.0230.0610.1030.085
Mother's qualification-0.0290.1670.1070.0160.2360.2050.4660.0870.1940.4251.0000.043-0.0120.1240.1440.092
Previous qualification0.0830.3880.440-0.1730.163-0.0140.0110.1460.2010.0130.0431.0000.0880.1090.1640.179
Previous qualification (grade)0.582-0.151-0.053-0.0400.148-0.0210.0040.072-0.026-0.023-0.0120.0881.0000.1000.1580.120
Scholarship holder0.1210.2290.2400.0420.1050.0590.1450.2280.1050.0610.1240.1090.1001.0000.4060.167
Target0.1700.2820.2880.1200.1310.0910.1350.3300.1060.1030.1440.1640.1580.4061.0000.447
Tuition fees up to date0.1220.2860.2820.0320.0990.0730.0830.1720.1270.0850.0920.1790.1200.1670.4471.000

Missing values

2025-11-17T11:44:03.376703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-17T11:44:03.456163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Marital statusApplication modeCourseDaytime/evening attendancePrevious qualificationPrevious qualification (grade)Mother's qualificationFather's qualificationMother's occupationFather's occupationAdmission gradeTuition fees up to dateGenderScholarship holderAge at enrollmentTarget
011923811126.011955122.610118Graduate
1117923811125.0191999119.810018Dropout
2117925411137.031923144.711018Dropout
311950011131.019332126.110118Enrolled
411950011132.0193749120.110018Graduate
5139171119133.1191911100.011024Dropout
61449085139130.0373796130.010021Graduate
711977311130.0193745133.911018Graduate
8139900311133.1191999130.001024Dropout
911950011135.0373749128.010118Graduate
Marital statusApplication modeCourseDaytime/evening attendancePrevious qualificationPrevious qualification (grade)Mother's qualificationFather's qualificationMother's occupationFather's occupationAdmission gradeTuition fees up to dateGenderScholarship holderAge at enrollmentTarget
7650811923811132.01143123.810019Graduate
7650911950011147.0371998140.710019Graduate
7651011977311130.012393153.611018Graduate
76511117977311138.011940134.210018Graduate
7651211923811134.0193847122.110019Graduate
76513117925411121.019175116.510118Graduate
7651411925411125.013849131.610019Graduate
76515517908511138.03737910123.310019Enrolled
7651611907011136.0383759124.810018Dropout
7651711977311133.1191949131.010019Graduate

Duplicate rows

Most frequently occurring

Marital statusApplication modeCourseDaytime/evening attendancePrevious qualificationPrevious qualification (grade)Mother's qualificationFather's qualificationMother's occupationFather's occupationAdmission gradeTuition fees up to dateGenderScholarship holderAge at enrollmentTarget# duplicates
3511914711131.0373799135.910018Enrolled5
2611914711130.0373799117.810018Enrolled4
8011950011132.0373777120.310118Graduate4
13511950011143.0319310126.811019Graduate4
237117950011143.038197130.610118Graduate4
3401449003139140.0373799140.011020Enrolled4
81117111141.011410142.810018Dropout3
1211908511131.011999139.810019Graduate3
1411908511131.0193799139.810119Graduate3
2811914711130.0373799122.010020Enrolled3